A Novel Bias Detection Technique for Partial Discharge Localization in Oil Insulation System

Abstract

This paper presents a sequential fault detection and identification algorithm for detecting a bias fault in the measurements of partial discharge (PD) in a transformer insulation system using acoustic signals. The algorithm identifies a bias fault in any of the ultrasonic sensors by computing the probability of having that bias fault given a carefully constructed measurement residual. The constructed measurement residual is a function of the measurement noise and the possible measurement fault. A set of bias hypotheses is assumed and initially given equal alarm probability. It is assumed that only one sensor will acquire a bias at any given time. Once the probability of a hypothesis approaches 1, that hypothesis is declared as the correct hypothesis and the bias associated with the hypothesis is removed from the sensors’ reading. Therefore, this will enable high-accuracy estimation of the location of PD. First, the algorithm is verified in a simulation environment. Subsequently, the accuracy of the proposed algorithm is verified using the experimental results.